As shown in Figure REF , an RNN often has a chain-like architecture, where in each period, the middle box is a neural network that takes \(x_t\)  and \(h_{t-1}\)  as the inputs and outputs \(h_t\)  and \(y_t\) . While many variations exist, a common implementation of an RNN can be described by the following updating equations [1]}:
\({h_t} &=& f\left( {W{h_{t - 1}} + U{x_t} + b} \right), \\{y_t} &=& g\left( {V{h_t} + c} \right), \) 
